add: agent template/prompt/tools

l1.py

@@ -0,0 +1,118 @@
+import asyncio
+from langchain.agents import create_react_agent, AgentExecutor
+from langchain.chat_models import init_chat_model
+from langchain_core.prompts import PromptTemplate
+from langchain_mcp_adapters.client import MultiServerMCPClient
+from langchain_core.tools import tool
+from langchain.memory import MongoDBChatMessageHistory, ConversationBufferWindowMemory
+
+from agents.agent_templates import AgentTemplate
+from agents.output_parser.MCPReactParser import MCPReactParser
+
+
+@tool
+def get_daily_string(salt: str):
+    """
+    :param salt: a random string
+    :return:get today's string
+    """
+    return "assss"
+
+
+async def main():
+
+    temp1 = AgentTemplate()
+    temp1.set_model("gpt-4.1-nano", "openai", "")
+    temp1.set_mcp_server("terminal_tools", {
+        'transport': 'sse',
+        'headers': {
+            "X-Api-Key": "bd303c2f2a515b4ce70c1f07ee85530c2"
+        },
+        'url': 'http://localhost:5050/sse'
+    })
+
+
+    mcp_client = MultiServerMCPClient({
+        'terminal_tool':{
+            'transport': 'sse',
+            'headers': {
+                "X-Api-Key": "bd303c2f2a515b4ce70c1f07ee85530c2"
+            },
+            'url': 'http://localhost:5050/sse'
+        }
+    })
+
+    tools = await mcp_client.get_tools()
+    xtools = []
+    for t in tools:
+        if t.name == 'CreateTerminalSession':
+            xtools.append(t)
+    xtools.append(get_daily_string)
+
+    print(xtools)
+    prompt = PromptTemplate.from_template("""
+    
+    {system}
+    
+    You have access to the following tools:
+
+    {tools}
+
+    Use the following format:
+
+    Question: the question you must answer
+    If you want to use tools:
+        Thought: always reason what to do
+        Action: the action to take, must be one of [{tool_names}]
+        Action Input: a **JSON object** with named arguments required by the tool
+        Observation: the result of the action
+    If no tool is needed:
+        Thought: what you are thinking
+    ... (this Thought/Action/... can repeat N times)
+    Final Answer: the final answer to the original question
+
+    Here is the conversation history:
+    {chat_history}
+    
+    User message: {user}
+    {agent_scratchpad}
+    """)
+    op = MCPReactParser()
+    agent = create_react_agent(model_openai, xtools, prompt, output_parser=op)
+    message_history = MongoDBChatMessageHistory(
+        connection_string=MONGO_CONNECTION_STRING,
+        session_id=USER_SESSION_ID,
+        database_name=MONGO_DB_NAME,
+        collection_name=MONGO_COLLECTION_NAME
+    )
+    mongodb_memory = ConversationBufferWindowMemory(
+        chat_memory=message_history,
+        memory_key=MEMORY_KEY,
+        k=10,
+        return_messages=False,
+    )
+    agent_executor = AgentExecutor(
+        agent=agent,
+        tools=xtools,
+        memory=mongodb_memory,
+        handle_parsing_errors=True,
+        verbose=True,
+
+    )
+    response = await agent_executor.ainvoke({
+        'system': 'you are a helpful assistant',
+        'user': 'hello, please create a terminal session with label \'x\' and show me the session id',
+    })
+    print(response)
+    r2 = await agent_executor.ainvoke({
+        'system': 'you are a helpful assistant',
+        'user': 'hello, please show me today\'s daily string'
+    })
+    print(r2)
+    #print(s1)
+    #print(s2)
+    #print(s3)
+    #print(s4)
+
+if __name__ == '__main__':
+    asyncio.run(main())